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A simple approach is proposed to obtain complexity controls for neural networks with general activation functions.
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Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
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Gaussian error linear units (GELUs)
Dan Hendrycks and Kevin Gimpel · 2016
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Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
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Approximation and estimation for high-dimensional deep learning networks
Andrew R Barron and Jason M Klusowski · 2018
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Barron spaces and the compositional function spaces for neural network models
Weinan E, Chao Ma, and Lei Wu · 2019
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Weinan E, Chao Ma, and Lei Wu · 2019
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A priori estimates of the population risk for two-layer neural networks
Weinan E, Chao Ma, and Lei Wu · 2019
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Total path variation for deep nets with general activation functions
Jason M. Klusowski · 2019
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Mean field limit of the learning dynamics of multilayer neural networks
Phan-Minh Nguyen · 2019
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Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2018
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A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro · 2018
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A priori estimates of the population risk for residual networks
Weinan E, Chao Ma, and Qingcan Wang · 2019
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How do infinite width bounded norm networks look in function space?
Pedro Savarese, Itay Evron, Daniel Soudry, and Nathan Srebro · 2019
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Mean field analysis of deep neural networks
Justin Sirignano and Konstantinos Spiliopoulos · 2019
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Global capacity measures for deep ReLU networks via path sampling
Ryan Theisen, Jason M Klusowski, Huan Wang, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2019
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